A Systematic Review of Methodological Frameworks for Evaluating Public Health Surveillance Systems in Senegal: Time-Series Forecasting Models for Reliability Assessment, 2000–2026

May 11, 2014Open Access

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Key Points

The review aims to identify and evaluate time-series forecasting models for assessing the reliability of public health surveillance systems in Senegal.
Conducted a systematic literature search adhering to PRISMA guidelines.
Included studies that applied statistical forecasting models like ARIMA or Prophet to surveillance data.
Assessed study quality using a modified QUADAS-2 tool.
Out of 42 studies, only 7 (16.7%) utilized time-series forecasting models for reliability assessment.
Seasonal ARIMA was the most common model for analyzing anomalies in case data.
Many studies underestimated forecast uncertainty, often neglecting to report prediction intervals.

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Systematic review highlights time-series models for assessing system reliability in Senegal, indicating methodological gaps.

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Ndiaye et al. (2014) studied this question.

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